Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.

Communications biology - 26 Oct 2024

Zhang Yuan, Dong Mingyuan, Deng Junsheng, Wu Jiafeng, Zhao Qiuye, Gao Xieping, Xiong Dapeng

Abstract excerpt

Assessing mutation impact on the binding affinity change (ΔΔG) of protein-protein interactions (PPIs) plays a crucial role in unraveling structural-functional intricacies of proteins and developing innovative protein designs. In this study, we present a deep learning framework, PIANO, for improved prediction of ΔΔG in PPIs. The PIANO framework leverages a graph masked self-distillation scheme for protein...

Topics

Join the communities discussing this publication.

Computational Biology
Deep Learning
Mutation
Protein Binding
Protein Interaction Mapping

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Open in PubMed Open full text

Identifiers

PMID: 39462102
DOI: 10.1038/s42003-024-07066-9
PMCID: PMC11513059

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

Community interpretation

Discuss the evidence

Ask a question, add context or challenge an interpretation. Community activity is separate from scientific evidence strength.

0

discussions

0

replies

Citations encouraged

Claims remain attributable.

No discussion yet

Be the first to add a careful, cited interpretation.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.PMID 39462102Read ↗

MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.Semantic proximity Read ↗

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation.Semantic proximity Read ↗

DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.Semantic proximity Read ↗

Mutation effect estimation on protein-protein interactions using deep contextualized representation learningSemantic proximity Read ↗

MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions.Semantic proximity Read ↗

Prot-ΔΔG: Prediction of protein-protein binding affinity changes upon mutations with pre-training strategies.Semantic proximity Read ↗

3D-ΔΔG: A Dual-Channel Prediction Model for Protein-Protein Binding Affinity Changes Following Mutation Based on Protein 3D Structures.Semantic proximity Read ↗

Sequence-aware Prediction of Point Mutation-induced Effects on Protein-Protein Binding Affinity using Deep LearningSemantic proximity Read ↗

MuLAN: Mutation-driven Light Attention Networks for investigating protein-protein interactions from sequencesSemantic proximity Read ↗

ProBASS – a language model with sequence and structural features for predicting the effect of mutations on binding affinitySemantic proximity Read ↗

Enhancing mutation impact prediction in protein-protein interactions through interpretable graph-based multi-level feature interactions.Semantic proximity Read ↗

Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.